Tutorials

Welcome to the athena tutorials! These guides will help you get started with building, training, and extending neural networks using the athena library.

Getting Started

• Building a Basic Network
• Training a Model
• Accessing Network Outputs
• Saving and Loading Models
• Inverse Design

Examples

• MNIST Classification Example
• Function Approximation (Regression)
• Residual Networks (ResNet)
• Message Passing Neural Networks
• Physics-Informed Neural Networks
• LNO Rollout Example
• Inverse Design Example
• wandb Logging and Hyperparameter Optimisation

Advanced Topics

• Creating Custom Layers
• Creating Custom Activation Functions
• Creating Custom Initialisers
• Creating Custom Optimisers
• Creating Custom Loss Functions

Next Steps

For specific information about layers, activations, and other components, see:

• Layers

• Activation Functions

• Initialisers

• Optimisers

• Loss Functions